stepEvolution function

Evolution of the stepwise model

Graphical representation of the selected terms using stepwise procedure for different values of the penalty parameter.

stepEvolution(X,Y,formula,P=1:7,K=10,test=NULL,graphic=TRUE)

Arguments

• X: a data.frame containing the design of experiments
• Y: a vector containing the response variable
• formula: a formula for the initial model
• P: a vector containing different values of the penalty parameter for which a stepwise selected model is fitted
• K: the number of folds for the cross-validation procedure
• test: an additional data set on which the prediction criteria are evaluated (default corresponds to no test data set)
• graphic: if TRUE the values of the criteria are represented

Returns

a list with the different criteria for different values of the penalty parameter. This list contains: - penalty: the values for the penalty parameter

• m: size m of the selected model for each value in P

• R2: the value of the R2 criterion for each model

According to the value of the test argument, other criteria are calculated:

a.	If a test set is available, R2test contains the value of the R2

criterion on the test set| |b.|If no test set is available, the Q2 and the RMSE computed by cross-validation are done.|

See Also

step procedure for linear models.

Note

Plots are also available. A tabular represents the selected terms for each value in P.

The evolution of the R2 criterion, the evolution of the size m of the selected model and criteria on the test set or by K-folds cross-validation are represented.

These graphical tools can be used to select the best value for the penalty parameter.

Author(s)

D. Dupuy

Examples

## Not run:

data(dataIRSN5D)
design <- dataIRSN5D[,1:5]
Y          <- dataIRSN5D[,6]
out        <- stepEvolution(design,Y,formulaLm(design,Y),P=c(1,2,5,10,20,30))
## End(Not run)